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With the advancements in reinforcement learning (RL), new variants of this artificial intelligence approach have been introduced in the literature. This has led to increased interest in using RL to address complex issues in diabetes management. Using RL, a decision maker (or agent) observes decision-making factors (or state) from the dynamic operating environment, selects actions, and subsequently receives delayed rewards. The agent adapts its actions to changes in the operating environment to maximize its cumulative reward and improve system performance. This paper presents how various variants of RL have been used to improve diabetes management, such as a higher time in range during which the blood glucose level is within the normal range and a higher similarity between RL and physician’s policies. Key highlights focus on the application of RL in diabetes management, including a taxonomy of the attributes of RL (e.g., roles and advantages), essential elements for training (e.g., data and simulators), representations of diabetes attributes in RL models, and variants of RL algorithms. In addition, this paper discusses open issues and potential future developments in the use of RL in diabetes management.

Vehicular edge computing (VEC) has been a new paradigm to support computation-intensive and latency-sensitive services. However, the scarcity of computational resources is still a challenge. Making efficient use of sporadic idle computational resources on smart vehicles in the vicinity to extend the resource capability of each vehicle is an important research issue. In this paper, we propose Virtual Edge, which is an efficient scheme to utilize free computational resources of multiple vehicles as a virtual server to facilitate collaborative vehicular edge computing. We design a virtual edge formation algorithm that considers both the stability of virtual edge and the computational resources available at the vehicles constituting the virtual edge. The prediction of the link duration between vehicles reduces the number of computation offloading failures caused by unexpected link disconnections. Extensive simulations with realistic vehicle movements are conducted to show the advantage of the proposed scheme over existing baselines in terms of the completion ratio of computation offloading tasks and average task execution time.

Vehicular Ad Hoc Network (VANET) emerges to ameliorate road accident, traffic congestion, and infotainment. Cognitive radio (CR) is integrated with VANET (termed as CR-VANET) to deal with the spectrum scarcity problems. For better data transmission, routing is very important in a highly dynamic CR-VANET environment. Routing in CR-VANET has several challenges due to vehicles’ high-speed movements and channel accessibility issues. This paper proposes a 2-Hop routing algorithm based on the Multi-Objective Harris Hawks Optimization (2HMO-HHO) algorithm that chooses the optimal forwarders between the source and destination vehicles. Selecting 2-hops instead of multiple hops or the entire route increases the selected route’s stability and assures successful data transmission. The simulations performed in OMNeT++ with SUMO show that the proposed algorithm achieves promising results on throughput, delay, packet delivery ratio, packet loss rate and communication overhead.

Augmented intelligence (AuI) integrates human intelligence (HI) and artificial intelligence (AI) to harness their strengths and mitigate their weaknesses. The combination of HI and AI has seen to improve both human and machine capabilities, and achieve a better performance compared to separate HI and AI approaches. In this paper, we present a survey of literature to understand how AuI has been applied in the literature, including the roles of HI and AI, AI approaches, features, and applications. Due to the limited literature related to this topic, we also present a survey of expert opinion to answer four main questions to understand the experts’ implications of AuI, including: a) the definition of AuI and the significance of HI in AuI; b) the roles of HI in AuI; c) the current and future applications of AuI in research, industry, and public, as well as the advantages and shortcomings of AuI; and d) end users’ view of the application of AuI. We also present recommendations to improve AuI, and provide a comparison between the findings from the surveys of both literature and expert opinion. The discussion of this paper shows the promising potential of AuI compared to separate HI and AI approaches.

Vehicular delay tolerant networks (VDTNs) enable information sharing among mobile nodes in scenarios where cellular base stations are unavailable and the connections between the mobile nodes are intermittent. While unmanned aerial vehicles (UAVs) have shown to improve the performance of VDTNs, existing routing protocols, such as PRoPHET, consider the encounter probability between mobile nodes only, which does not fully address the characteristics of UAVs in route selection. In this paper, we propose a routing protocol, which considers both the encounter probability and the persistent connection time between mobile nodes for each encounter, in UAV-assisted VDTNs. By introducing the persistent connection time in route selection, the proposed protocol is able to evaluate the stability of a communication link more accurately in the UAV-assisted VDTN environment. The proposed protocol is evaluated using realistic simulations by comparing it with existing baselines. The simulation results verify that the proposed protocol can improve the reliability of message forwarding while reducing network overhead and end-to-end delay.

Artificial intelligence (AI) is a powerful tool for technological progress with profound impacts on governments, industries, universities, and societies. In Southeast Asia, AI has presented both opportunities and challenges. Local governments are increasingly experimenting and piloting AI technology to create smart, sustainable, and inclusive cities. Nevertheless, the adoption of artificial intelligence in cities requires proper planning, management, and implementation. Consequently, there is a need for a concerted effort to establish collaboration between industries, governments, academics, and societies in Southeast Asia. This policy brief aims to provide an overview of how artificial intelligence can be implemented to enhance urban mobility in Southeast Asia. To maximize the relevance of the policy brief, experts from various fields have been consulted in its preparation.

Federated learning (FL) is a distributed machine learning approach that can achieve the purpose of collaborative learning from a large amount of data that belong to different parties without sharing the raw data among the data owners. FL can sufficiently utilize the computing capabilities of multiple learning agents to improve the learning efficiency while providing a better privacy solution for the data owners. FL attracts tremendous interests from a large number of industries due to growing privacy concerns. Future vehicular Internet of Things (IoT) systems, such as cooperative autonomous driving and intelligent transport systems (ITS), feature a large number of devices and privacy-sensitive data where the communication, computing, and storage resources must be efficiently utilized. FL could be a promising approach to solve these existing challenges. In this paper, we first conduct a brief survey of existing studies on FL and its use in wireless IoT. Then, we discuss the significance and technical challenges of applying FL in vehicular IoT, and point out future research directions.

In order to achieve a greener intelligent transport system (ITS), an efficient collaboration between vehicles is required to manage computation task processing with low latency. In this paper, we propose a collaborative edge computing scheme for vehicular Internet-of-things towards a greener ITS. The proposed scheme uses some vehicles as edge nodes, which are responsible for finding task processor nodes on behalf of a task requester node by considering the end-to-end task response time. The proposed scheme employs a two-stage approach where the first stage enables an efficient networking and computing architecture by forming vehicle clusters based on the edge architecture, and the second stage optimizes offloading tasks based on the architecture. We use realistic computer simulations to compare the proposed scheme with existing baselines, and show its superiority in terms of task offloading performance.

Nowadays, machine learning (ML), which is one of the most rapidly growing technical tools, is extensively used to solve critical challenges in various domains. Vehicular ad hoc network (VANET) is expected to be the key role player in reducing road casualties and traffic congestion. To ensure this role, a gigantic amount of data should be exchanged. However, current allocated wireless access for VANET is inadequate to handle such massive data amounts. Therefore, VANET faces a spectrum scarcity issue. Cognitive radio (CR) is a promising solution to overcome such an issue. CR-based VANET or CR-VANET must achieve several performance enhancement measures, including ultra-reliable and low-latency communication. ML methods can be integrated with CR-VANET to make CR-VANET highly intelligent, achieve rapid adaptability to the dynamicity of the environment, and improve the quality of service in an energy-efficient manner. This paper presents an overview of ML, CR, VANET, and CR-VANET, including their architectures, functions, challenges, and open issues. The applications and roles of ML methods in CR-VANET scenarios are reviewed. Insights into the use of ML for autonomous or driver-less vehicles are also presented. Current advancements in the amalgamation of these prominent technologies and future research directions are discussed.

In this digital age, ports face stiff competition in global supply chain. Smart ports, as high performing ports, utilize information and communications technology (ICT) to provide a wide range of smart applications, resulting in vastly improved vessels and container management among others, which subsequently improve the competitiveness and sustainability of the national economy. While various novel solutions, such as information system and locating system, have been proposed to improve smart port activities, there are several key issues pertaining to ports and port operations that warrant specific attention, particularly greenhouse gases emission, which has accelerated to an alarming level. The urgent need to address such issues is lacking. This article aims to offer a review of the research literature on smart ports, including Internet of Things platform, greenhouse gases emission reduction, energy efficiency enhancement, and so on. The objective is to establish a foundation of existing research conducted on smart ports in order to motivate new research interests in this area. Open issues are also presented to foster new research initiatives on smart ports.

Trust represents the belief or perception of an entity, such as a mobile device or a node, in the extent to which future actions and reactions are appropriate in a collaborative relationship. Reputation represents the network-wide belief or perception of the trustworthiness of an entity. Each entity computes and assigns a trust or reputation value, which increases and decreases with the appropriateness of actions and reactions, to another entity in order to ensure a healthy collaborative relationship. Trust and reputation management (TRM) has been investigated to improve the security of traditional networks, particularly the access networks. In 5G, the access networks are multi-hop networks formed by entities which may not be trustable, and so such networks are prone to attacks, such as Sybil and crude attacks. TRM addresses such attacks to enhance the overall network performance, including reliability, scalability, and stability. Nevertheless, the investigation of TRM in 5G, which is the next-generation wireless networks, is still at its infancy. TRM must cater for the characteristics of 5G. Firstly, ultra-densification due to the exponential growth of mobile users and data traffic. Secondly, high heterogeneity due to the different characteristics of mobile users, such as different transmission characteristics (e.g., different transmission power) and different user equipment (e.g., laptops and smartphones). Thirdly, high variability due to the dynamicity of the entities' behaviors and operating environment. TRM must also cater for the core features of 5G (e.g., millimeter wave transmission, and device-to-device communication) and the core technologies of 5G (e.g., massive MIMO and beamforming, and network virtualization). In this paper, a review of TRM schemes in 5G and traditional networks, which can be leveraged to 5G, is presented. We also provide an insight on some of the important open issues and vulnerabilities in 5G networks that can be resolved using a TRM framework.

In order to enable emerging vehicular Internet of Things (IoT) applications, including fully autonomous driving, more efforts should be done in collecting driving experiences in different road situations. This requires the exchange of information between vehicles as each vehicle has very limited experience. Due to the decentralized feature of vehicular environment, an efficient management of collaborative behaviors among the vehicles becomes particularly important. Blockchain has been attracting great interest recently because it provides a way to reach consensus in decentralized systems. However, existing blockchain systems assume high communication capabilities for vehicles, which is difficult to achieve in a decentralized vehicular environment. Existing studies also assume the existence of networking infrastructure, such as roadside units (RSU). In this paper, we propose a scheme to empower blockchain in vehicular environments without depending on the existing networking infrastructure. The proposed scheme uses a distributed clustering approach to select some vehicles as edge nodes, and the edge nodes maintain the blockchain used to record transactions in a decentralized way. The proposed scheme employs a distributed approach that guides vehicle clustering with the consideration of multiple metrics based on a fuzzy logic algorithm. By using the edge nodes, the proposed scheme solves the communication problem of maintaining a blockchain in a totally decentralized vehicular environment. We use computer simulations to clarify the performance of the proposed scheme in terms of communication performance by comparing it with existing baselines.

Traffic congestion is a complex, vexing, and growing issue day by day in most urban areas worldwide. The integration of the newly emerging deep learning approach and the traditional reinforcement learning approach has created an advanced approach called deep reinforcement learning (DRL) that has shown promising results in solving high-dimensional and complex problems, including traffic congestion. This article presents a review of the attributes of traffic signal control (TSC), as well as DRL architectures and methods applied to TSC, which helps to understand how DRL has been applied to address traffic congestion and achieve performance enhancement. The review also covers simulation platforms, a complexity analysis, as well as guidelines and design considerations for the application of DRL to TSC. Finally, this article presents open issues and new research areas with the objective to spark new interest in this research field. To the best of our knowledge, this is the first review article that focuses on the application of DRL to TSC.

Cognitive radio network (CRN) is the next generation wireless network that allows unlicensed users [secondary users (SUs)] to explore and use the underutilised licensed channels (white spaces) owned by licensed users (primary users). The purpose is to increase the spectrum utilization for enhanced network performance. Clustering segregates SUs in a CRN into logical groups (clusters) with each consisting of a leader (cluster head) and member nodes. A budget-based cluster size adjustment scheme is applied to enable each cluster to adjust its number of member nodes in its cluster based on the availability of white spaces in order to improve network scalability. However, cluster size adjustment is prone to attacks by malicious SUs that launch random and intelligent attacks. Hence, we incorporate an artificial intelligence approach called reinforcement learning (RL) into a trust model to countermeasure the random and intelligent attacks. The simulation results show that RL-based trust model increases the utilization of white spaces and cluster size to improve network scalability and enhance network performance despite the presence of RL-based intelligent attacks.

The integration of simultaneous wireless information and power transfer (SWIPT) and cooperative relay (CoR) techniques has evolved as a new phenomenon for the next-generation wireless communication system. CoR is used to get energy and spectral efficient network and to solve the issues of fading, path loss, shadowing, and smaller coverage area. Relay nodes are battery-constrained or battery-less devices. They need some charging systems externally as replacing or recharging of their batteries sometimes which are not feasible and convenient. Energy harvesting (EH) is the most cost-effective, suitable, and safer solutions to power up these relays. Among various types of the EH, SWIPT is the most prominent technique as it provides spectral efficiency by delivering energy and information to the relays at the same time. This paper reviews the combination of CoR and SWIPT. From basic to advanced architectures, applications and taxonomies of CoR and SWIPT are presented, various forms of resource allocation and relay selection algorithms are covered. The usage of CoR and SWIPT in the fifth-generation wireless networks is discussed. This paper focuses on the integral aspects of the CoR and SWIPT to other next-generation wireless communication systems and techniques such as multiple-input-multiple-output, wireless sensor network, cognitive radio, vehicular ad hoc network, non-orthogonal multiple access, beamforming technique, and the Internet of Things. Some open issues and future directions and challenges are given in this paper.

Maritime networks establish wireless multi-hop networks to provide wireless broadband service at sea, connecting various kinds of ships, maritime buoys, and beacons. The maritime networks possess two distinguishing characteristics highly affected by maneuver at sea-dynamic link quality and bandwidth constrained, and dynamic network topology-that warrant specific attention. Unlike land vehicles, maneuver at sea is affected by sea surface movement and wave occlusions, which can cause unstable environment with a high rate of link breakages caused by low link stability, as well as low and highly variable bandwidth. In spite of the need to achieve performance close to high-speed terrestrial wireless broadband service on land, there is only a perfunctory effort to investigate maritime networks. There is an urgent need to refresh the interest to investigate, as well as to further enhance, maritime networks. This paper presents a review of the limited research works of this topic, which revolve around the networking issues in the link, network, and upper layers, in the literature. The objective is to establish a foundation in order to motivate a new research interest in maritime networks. Open issues are also presented to foster new research initiatives in this burgeoning and exciting area.

5G is the next generation cellular network that aspires to achieve substantial improvement on quality of service, such as higher throughput and lower latency. Edge computing is an emerging technology that enables the evolution to 5G by bringing cloud capabilities near to the end users (or user equipment, UEs) in order to overcome the intrinsic problems of the traditional cloud, such as high latency and the lack of security. In this paper, we establish a taxonomy of edge computing in 5G, which gives an overview of existing state-of-the-art solutions of edge computing in 5G on the basis of objectives, computational platforms, attributes, 5G functions, performance measures, and roles. We also present other important aspects, including the key requirements for its successful deployment in 5G and the applications of edge computing in 5G. Then, we explore, highlight, and categorize recent advancements in edge computing for 5G. By doing so, we reveal the salient features of different edge computing paradigms for 5G. Finally, open research issues are outlined.

While machine learning and artificial intelligence have long been applied in networking research, the bulk of such works has focused on supervised learning. Recently, there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services, such as traffic engineering, anomaly detection, Internet traffic classification, and quality of service optimization. The growing interest in applying unsupervised learning techniques in networking stems from their great success in other fields, such as computer vision, natural language processing, speech recognition, and optimal control (e.g., for developing autonomous self-driving cars). In addition, unsupervised learning can unconstrain us from the need for labeled data and manual handcrafted feature engineering, thereby facilitating flexible, general, and automated methods of machine learning. The focus of this survey paper is to provide an overview of applications of unsupervised learning in the domain of networking. We provide a comprehensive survey highlighting recent advancements in unsupervised learning techniques, and describe their applications in various learning tasks, in the context of networking. We also provide a discussion on future directions and open research issues, while identifying potential pitfalls. While a few survey papers focusing on applications of machine learning in networking have previously been published, a survey of similar scope and breadth is missing in the literature. Through this timely review, we aim to advance the current state of knowledge, by carefully synthesizing insights from previous survey papers, while providing contemporary coverage of the recent advances and innovations.

Healthcare is undergoing a rapid transformation from traditional hospital and specialist focused approach to a distributed patient-centric approach. Advances in several technologies fuel this rapid transformation of healthcare vertical. Among various technologies, communication technologies have enabled to deliver personalized and remote healthcare services. At present, healthcare widely uses the existing 4G network and other communication technologies for smart healthcare applications and are continually evolving to accommodate the needs of future intelligent healthcare applications. As the smart healthcare market expands the number of applications connecting to the network will generate data that will vary in size and formats. This will place complex demands on the network in terms of bandwidth, data rate, and latency, among other factors. As this smart healthcare market matures, the connectivity needs for a large number of devices and machines with sensor-based applications in hospitals will necessitate the need to implement Massive-Machine Type Communication. Further use cases such as remote surgeries and Tactile Internet will spur the need for Ultra Reliability and Low Latency Communications or Critical Machine Type Communication. The existing communication technologies are unable to fulfill the complex and dynamic need that is put on the communication networks by the diverse smart healthcare applications. Therefore, the emerging 5G network is expected to support smart healthcare applications, which can fulfill most of the requirements such as ultra-low latency, high bandwidth, ultra-high reliability, high density, and high energy efficiency. The future smart healthcare networks are expected to be a combination of the 5G and IoT devices which are expected to increase cellular coverage, network performance and address security-related concerns. This paper provides a state-of-the-art review of the 5G and IoT enabled smart healthcare, Taxonomy, research trends, challenges, and future research directions.

With the significant rise in demand for network utilization, such as data transmission and device-to-device (D2D) communication, fifth-generation (5G) networks have been proposed to fill the demand. Deploying 5G enhances the utilization of network channels and allows users to exploit licensed channels in the absence of primary users (PUs). In this paper, a hybrid route selection mechanism is proposed, and it allows the central controller (CC) to evaluate the route map proactively in a centralized manner for source nodes. In contrast, source nodes are enabled to make their own decisions reactively and select a route in a distributed manner. D2D communication is preferred, which helps networks to offload traffic from the control plane to the data plane. In addition to the theoretical analysis, a real testbed was set up for the proof of concept; it was composed of eleven nodes with independent processing units. Experiment results showed improvements in traffic offloading, higher utilization of network channels, and a lower interference level between primary and secondary users. Packet delivery ratio and end-to-end delay were affected due to a higher number of intermediate nodes and the dynamicity of PU activities.

Flying ad hoc network (FANET) is an application of 5G access network, which consists of unmanned aerial vehicles or flying nodes with scarce resources and high mobility rates. This paper proposes a deep Q-network (DQN)-based vertical routing scheme to select routes with higher residual energy levels and lower mobility rates across network planes (i.e., macro-plane, pico-plane, and femto-plane), which has not been investigated in the literature. The main motivation behind this work is to address frequent link disconnections and network partitions in order to enhance network performance. The 5G access network has a central controller (CC) and distributed controllers (DCs) in different network planes. The proposed scheme is a hybrid approach that allows CC and DCs to exchange information among themselves, and handle global and local information, respectively. The proposed scheme is suitable for highly dynamic ad hoc FANETs, and it enables data communication between UAVs in various applications, such as monitoring and performing surveillance of borders, and targeted-based operations (e.g., object tracking). Vertical routing is performed over a clustered network, in which clusters are formed across different network planes to provide inter-plane and inter-cluster communications. This helps to offload data traffic across different network planes to enhance network lifetime. Compared to the traditional reinforcement learning approach, the proposed DQN-based vertical routing scheme has shown to increase network lifetime by up to 60%, reduce energy consumption by up to 20%, and reduce the rate of link breakages by up to 50%.

Cognitive radio (CR) enables unlicensed users to sense for and access underutilized licensed channels (or white spaces) owned by the licensed users in an opportunistic manner. Clustering segregates nodes in a network into logical groups called clusters. In CR networks (CRNs), larger cluster size improves network scalability thereby contributing to reduced routing overhead; however, it reduces cluster stability as the number of available common channels in a cluster reduces resulting in increased number of re-clusterings and clustering overhead. This paper presents our proposed first-of-its-kind cluster size adjustment scheme based on an artificial intelligence approach called reinforcement learning. The proposed scheme adapts the cluster size with the amount of white spaces as time goes by in order to improve network scalability and cluster stability in CRNs. Due to the lack of progress in the investigation of cluster size adjustment schemes in the literature, this paper also analyzes their attributes, and then presents such schemes investigated in various kinds of distributed wireless networks. Simulation results show that our proposed scheme improves network scalability by creating larger clusters, and improves cluster stability by reducing the number of re-clusterings (i.e., the number of cluster splits) and clustering overhead, while reducing interference between licensed and unlicensed users in CRNs.

Despite the evolution of cellular networks, spectrum scarcity and the lack of intelligent and autonomous capabilities remain a cause for concern. These problems have resulted in low network capacity, high signaling overhead, inefficient data forwarding, and low scalability, which are expected to persist as the stumbling blocks to deploy, support and scale next-generation applications, including smart city and virtual reality. Fifth-generation (5G) cellular networking, along with its salient operational characteristics - including the cognitive and cooperative capabilities, network virtualization, and traffic offload - can address these limitations to cater to future scenarios characterized by highly heterogeneous, ultra-dense, and highly variable environments. Cognitive radio (CR) and cognition cycle (CC) are key enabling technologies for 5G. CR enables nodes to explore and use underutilized licensed channels; while CC has been embedded in CR nodes to learn new knowledge and adapt to network dynamics. CR and CC have brought advantages to a cognition-inspired 5G cellular network, including addressing the spectrum scarcity problem, promoting interoperation among heterogeneous entities, and providing intelligence and autonomous capabilities to support 5G core operations, such as smart beamforming. In this paper, we present the attributes of 5G and existing state of the art focusing on how CR and CC have been adopted in 5G to provide spectral efficiency, energy efficiency, improved quality of service and experience, and cost efficiency. This main contribution of this paper is to complement recent work by focusing on the networking aspect of CR and CC applied to 5G due to the urgent need to investigate, as well as to further enhance, CR and CC as core mechanisms to support 5G. This paper is aspired to establish a foundation and to spark new research interest in this topic. Open research opportunities and platform implementation are also presented to stimulate new research initiatives in this exciting area.

The operation of modern electronic communication devices relies on constant energy sources such as DC power, obtained from AC sources via AC to DC conversion, or small chargeable/replaceable batteries. However, in some cases, providing constant energy sources may not be feasible. Advanced energy harvesting technologies can be used to power up modern day electronic communication devices [item 1) in the Appendix]. These energy harvesting technologies rely on energy sources naturally present in the environment, such as solar energy, wind energy, and heat [item 2) in the Appendix]. Moreover, energy can also be acquired from the movement of different parts of the human body such as legs (walking), heart (beating) and arms (swinging), just to name a few. All these energy harvesting technologies and techniques may satisfy the need of energy for low power communication devices and may enable the charging of electronic mobile devices anywhere and at any time [item 3) in the Appendix].

Recent advancements in deep reinforcement learning (DRL) have led to its application in multi-agent scenarios to solve complex real-world problems, such as network resource allocation and sharing, network routing, and traffic signal controls. Multi-agent DRL (MADRL) enables multiple agents to interact with each other and with their operating environment, and learn without the need for external critics (or teachers), thereby solving complex problems. Significant performance enhancements brought about by the use of MADRL have been reported in multi-agent domains; for instance, it has been shown to provide higher quality of service (QoS) in network resource allocation and sharing. This paper presents a survey of MADRL models that have been proposed for various kinds of multi-agent domains, in a taxonomic approach that highlights various aspects of MADRL models and applications, including objectives, characteristics, challenges, applications, and performance measures. Furthermore, we present open issues and future directions of MADRL.

Based on the literature, we present an artificial intelligence marketing (AIM) framework that enables autonomous machines to receive big data and information, use artificial intelligence (AI) to create knowledge, and then disseminate and apply the knowledge to enhance customer relationships in a knowledge-based environment. To develop the AIM framework, we bring together and curate a wide range of relevant literatures including real-life examples and cases, and then understand how these literatures contribute to the framework in this research topic. We explain the AIM framework from the interdisciplinary perspective, which is an important role of both the artificial intelligence and marketing academia. The AIM framework includes three main components, including the pre-processor, the main processor, and the memory storage. The main processor, which is the key component, uses AI to process structured data processed by pre-processor in order to make real-time decisions and reasonings. The AI approach is characterized by its hypothetical abilities, learning paradigms, and operation modes with human. The strategic use of the developed AIM framework based on the literature to enhance customer relationships, including customer trust, satisfaction, commitment, engagement, and loyalty, is presented. Finally, future potential investigations are presented to drive forward this interdisciplinary research topic.

A vehicle ad hoc network (VANET) is a solution for road safety, congestion management, and infotainment services. Integration of cognitive radio (CR), known as CR-VANET, is needed to solve the spectrum scarcity problems of VANET. Several research efforts have addressed the concerns of CR-VANET. However, more reliable, robust, and faster spectrum sensing is still a challenge. A novel segment-based CR-VANET (Seg-CR-VANET) architecture is therefore proposed in this paper. Roads are divided equally into segments, and they are sub-segmented based on the probability value. Individual vehicles or secondary users produce local sensing results by choosing an optimal spectrum sensing (SS) technique using a hybrid machine learning algorithm that includes fuzzy and naïve Bayes algorithms. We used dynamic threshold values for the sensing techniques. In this proposed cooperative SS, the segment spectrum agent (SSA) made the global decision using the tri-agent reinforcement learning (TA-RL) algorithm. Three environments (network, signal, and vehicle) are learned by this proposed algorithm to determine primary (licensed) users’ activities. The simulation results indicate that, compared to current works, the proposed Seg-CR-VANET produces better results in spectrum sensing.

Cognitive radio (CR) enables unlicensed users to explore and exploit underutilized licensed channels (or white spaces). While multi-hop CR network has drawn significant research interest in recent years, majority work has been validated through simulation. A key challenge in multi-hop CR network is to select a route with high quality of service (QoS) and lesser number of route breakages. In this paper, we propose three route selection schemes to enhance the network performance of CR networks, and investigate them using a real testbed environment, which consists of universal software radio peripheral and GNU radio units. Two schemes are based on reinforcement learning (RL), while a scheme is based on spectrum leasing (SL). RL is an artificial intelligence technique, whereas SL is a new paradigm that allows communication between licensed and unlicensed users in CR networks. We compare the route selection schemes with an existing route selection scheme in the literature, called highest-channel (HC), in a multi-hop CR network. With respect to the QoS parameters (i.e., throughput, packet delivery ratio, and the number of route breakages), the experimental results show that RL approaches achieve a better performance in comparison with the HC approach, and also achieve close to the performance achieved by the SL approach.

The vehicular Internet of Things (IoT) comprises enabling technologies for a large number of important applications including collaborative autonomous driving and advanced transportation systems. Due to the mobility of vehicles, strict application requirements, and limited communication resources, the conventional centralized control fails to provide sufficient quality of service for connected vehicles, so a decentralized approach is required in the vicinity to satisfy the requirements of delay-sensitive and mission-critical applications. A decentralized system is also more resistant to the single point of failure problem and malicious attacks. Blockchain technology has been attracting great interest due to its capability of achieving a decentralized, transparent, and tamper-resistant system. There are many studies focusing on the use of blockchain in managing data and transactions in vehicular environments. However, the application of blockchain in vehicular environments also faces some technical challenges. In this paper, we first explain the fundamentals of blockchain and vehicular IoT. Then, we conduct a literature review on the existing research efforts of the blockchain for vehicular IoT by discussing the research problems and technical issues. After that, we point out some future research issues considering the characteristics of both blockchain and vehicular IoT.

With today’s computer networks becoming increasingly dynamic, heterogeneous, and complex, there is great interest in deploying artificial intelligence (AI) based techniques for optimization and management of computer networks. AI techniques—that subsume multidisciplinary techniques from machine learning, optimization theory, game theory, control theory, and meta-heuristics—have long been applied to optimize computer networks in many diverse settings. Such an approach is gaining increased traction with the emergence of novel networking paradigms that promise to simplify network management (e.g., cloud computing, network functions virtualization, and software-defined networking) and provide intelligent services (e.g., future 5G mobile networks). Looking ahead, greater integration of AI into networking architectures can help develop a future vision of cognitive networks that will show network-wide intelligent behavior to solve problems of network heterogeneity, performance, and quality of service (QoS).

Cognitive Radio (CR) is a dynamic spectrum access approach, in which unlicensed users (or secondary users, SUs) exploit the underutilized channels (or white spaces) owned by the licensed users (or primary users, PUs). Traditionally, SUs are oblivious to PUs, and therefore the acquisition of white spaces is not guaranteed. Hence, a SU must vacate its channel whenever a PU reappears on it in an unpredictable manner, which may affect the SUs' network performance. Spectrum leasing has been proposed to tackle the aforementioned problem through negotiation between the PU and SU networks, which allows the SUs to acquire white spaces for a guaranteed period of time. Through spectrum leasing, the PUs and SUs enhance their network performances, and additionally PUs maximize their respective monetary gains. Numerous research efforts have been made to investigate the CR, whereas the research into spectrum leasing remains at its infancy. In this paper, we present a comprehensive review on spectrum leasing schemes in CR networks by highlighting some pioneering approaches and discuss the gains, functionalities, characteristics, and challenges of spectrum leasing schemes along with the performance enhancement in CR networks. Additionally, we discuss various open issues in order to spark new interests in this research area.

Cognitive radio (CR) enables unlicensed users to exploit the underutilized spectrum in licensed spectrum whilst minimizing interference to licensed users. Reinforcement learning (RL), which is an artificial intelligence approach, has been applied to enable each unlicensed user to observe and carry out optimal actions for performance enhancement in a wide range of schemes in CR, such as dynamic channel selection and channel sensing. This paper presents new discussions of RL in the context of CR networks. It provides an extensive review on how most schemes have been approached using the traditional and enhanced RL algorithms through state, action, and reward representations. Examples of the enhancements on RL, which do not appear in the traditional RL approach, are rules and cooperative learning. This paper also reviews performance enhancements brought about by the RL algorithms and open issues. This paper aims to establish a foundation in order to spark new research interests in this area. Our discussion has been presented in a tutorial manner so that it is comprehensive to readers outside the specialty of RL and CR.

Cognitive radio (CR) enables unlicensed users (or secondary users, SUs) to sense for and exploit underutilized licensed spectrum owned by the licensed users (or primary users, PUs). Reinforcement learning (RL) is an artificial intelligence approach that enables a node to observe, learn, and make appropriate decisions on action selection in order to maximize network performance. Routing enables a source node to search for a least-cost route to its destination node. While there have been increasing efforts to enhance the traditional RL approach for routing in wireless networks, this research area remains largely unexplored in the domain of routing in CR networks. This paper applies RL in routing and investigates the effects of various features of RL (i.e., reward function, exploitation, and exploration, as well as learning rate) through simulation. New approaches and recommendations are proposed to enhance the features in order to improve the network performance brought about by RL to routing. Simulation results show that the RL parameters of the reward function, exploitation, and exploration, as well as learning rate, must be well regulated, and the new approaches proposed in this paper improves SUs’ network performance without significantly jeopardizing PUs’ network performance, specifically SUs’ interference to PUs.